Study Report

Cross-Disorder Group of the Psychiatric Genomics Consortium (2013). "Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis." Lancet 381(9875): 1371-1379.

We analysed genome-wide single-nucleotide polymorphism (SNP) data for the five disorders in 33,332 cases and 27,888 controls of European ancestory. To characterise allelic effects on each disorder, we applied a multinomial logistic regression procedure with model selection to identify the best-fitting model of relations between genotype and phenotype. We examined cross-disorder effects of genome-wide significant loci previously identified for bipolar disorder and schizophrenia, and used polygenic risk-score analysis to examine such effects from a broader set of common variants. We undertook pathway analyses to establish the biological associations underlying genetic overlap for the five disorders. We used enrichment analysis of expression quantitative trait loci (eQTL) data to assess whether SNPs with cross-disorder association were enriched for regulatory SNPs in post-mortem brain-tissue samples.

Total Sample

The sample for these analyses consisted of cases, controls, and family-based samples assembled for previous genome-wide PGC mega-analyses of individuallevel data.

Sample Collection

The sample for these analyses consisted of cases, controls, and family-based samples assembled for previous genome-wide PGC mega-analyses of individuallevel data.

Diagnosis Description

DSM third edition revised or fourth edition

Technique

Raw genotype and phenotype data for each study were uploaded to a central server and processed through the same quality control, imputation, and analysis process. We analysed imputed SNP dosages from 1 250 922 autosomal SNPs.

Analysis Method

In the primary analysis, we combined effects of each disease analysis by a meta-analytic approach that applied a weighted Z-score; In a second analytical approach, we did a five-degree-offreedom test by summing the chi square values for each individual disease meta-analysis. To characterise the specificity of the allelic effects for our main findings, we examined the association evidence in three ways: we generated forest plots of the disorder beta coefficients with 95% CIs; we calculated a heterogeneity p value for the disorder-specific effects contributing to the overall statistics for meta-analytic association; and we undertook a multinomial logistic regression procedure with model selection for each main SNP for all five disorders to assess the pattern of phenotypic effects.

Result Description

SNPs at four loci surpassed the cutoff for genome-wide significance (p<5x10(-8)) in the primary analysis: regions on chromosomes 3p21 and 10q24, and SNPs within two L-type voltage-gated calcium channel subunits, CACNA1C and CACNB2. Model selection analysis supported effects of these loci for several disorders. Loci previously associated with bipolar disorder or schizophrenia had variable diagnostic specificity. Polygenic risk scores showed cross-disorder associations, notably between adult-onset disorders. Pathway analysis supported a role for calcium channel signalling genes for all five disorders. Finally, SNPs with evidence of cross-disorder association were enriched for brain eQTL markers. Our findings show that specific SNPs are associated with a range of psychiatric disorders of childhood onset or adult onset. In particular, variation in calcium-channel activity genes seems to have pleiotropic effects on psychopathology. These results provide evidence relevant to the goal of moving beyond descriptive syndromes in psychiatry, and towards a nosology informed by disease cause.